U.S. patent application number 13/339302 was filed with the patent office on 2012-04-26 for systems and methods for reducing energy usage,.
Invention is credited to Bao Tran.
Application Number | 20120101653 13/339302 |
Document ID | / |
Family ID | 45973656 |
Filed Date | 2012-04-26 |
United States Patent
Application |
20120101653 |
Kind Code |
A1 |
Tran; Bao |
April 26, 2012 |
SYSTEMS AND METHODS FOR REDUCING ENERGY USAGE,
Abstract
A system for reducing energy usage includes one or more utility
meters each capturing energy load data on at least a 15 minute
interval basis; a usage disaggregator coupled to one or more of the
utility meters to disaggregate energy consumption for one or more
predetermined appliances based on the electrical load interval
signatures of each predetermined appliance; and an energy messaging
module coupled to the energy usage disaggregator to help users
reduce energy consumption.
Inventors: |
Tran; Bao; (Saratoga,
CA) |
Family ID: |
45973656 |
Appl. No.: |
13/339302 |
Filed: |
December 28, 2011 |
Current U.S.
Class: |
700/296 |
Current CPC
Class: |
G01D 4/004 20130101;
Y04S 20/242 20130101; Y04S 20/222 20130101; H02J 2310/14 20200101;
Y02B 90/20 20130101; Y02B 70/30 20130101; Y02B 70/3225 20130101;
Y04S 20/30 20130101 |
Class at
Publication: |
700/296 |
International
Class: |
G05D 11/00 20060101
G05D011/00 |
Claims
1. A system for reducing energy usage, comprising: one or more
utility meters, each transmitting energy load data with at least a
fifteen minute data interval basis; an energy usage disaggregator
coupled to one or more of the utility meters to disaggregate energy
consumption based on electrical load signatures of each
predetermined appliance; and an energy messaging module coupled to
the energy usage disaggregator to help users reduce energy
consumption.
2. The system of claim 1, wherein the usage disaggregator
identifies heat usage and air conditioning usage.
3. The system of claim 1, wherein the usage disaggregator
identifies water usage by a predetermined appliance.
4. The system of claim 1, wherein the usage disaggregator
identifies gas usage by a predetermined appliance.
5. The system of claim 1, wherein the messaging module suggests
actions to take to reduce energy consumption.
6. The system of claim 1, comprising a controller coupled to air
conditioning or heating appliances to save energy.
7. The system of claim 1, comprising a normative mailer to engage
and motivate action from targeted individuals.
8. The system of claim 7, wherein the normative mailer encourages
people to take energy-saving actions with suggestions on reducing
power consumption.
9. The system of claim 1, wherein the usage disaggregator derives
from utility meter readings energy usage for air conditioning, air
heating, refrigerating, lighting, or water heating.
10. The system of claim 1, comprising an energy usage predictor
communicating with a utility computer to perform demand
response.
11. A method to reduce energy usage, comprising: reading energy
load data from utility meters, each meter transmitting data with at
least a fifteen minute data interval basis; disaggregating energy
consumption for each of predetermined appliances from the interval
of energy load data; and normatively messaging users to reduce
energy consumption.
12. The method of claim 11, comprising identifying heat usage and
air conditioning usage.
13. The method of claim 11, comprising identifying lighting energy
usage.
14. The method of claim 11, comprising identifying gas usage by a
predetermined appliance.
15. The method of claim 11, comprising suggesting actions to take
to reduce energy consumption.
16. The method of claim 11, comprising controlling to air
conditioning or heating appliances based on the disaggregated
energy consumption to save energy.
17. The method of claim 11 comprising mailing users to engage in
energy saving and motivating action from users.
18. The method of claim 11, comprising deriving from utility meter
readings energy usage for air conditioning, air heating,
refrigerating, lighting, or water heating.
19. The method of claim 11, comprising predicting energy usage and
performing demand response in accordance with the predicted energy
usage.
20. A system for optimizing energy usage, comprising: one or more
utility meters to generate electrical load data with at least a
fifteen minute data interval basis; a load disaggregator receiving
the electrical load interval data from each utility meter to
identify power consumption from each of a group of predetermined
appliances; and an energy messaging module coupled to the load
disaggregator to generate normative energy saving messages to
users.
Description
[0001] The present application claims priority to U.S. application
Ser. No. 12/871,638, filed Aug. 30, 2010, the content of which is
incorporated by reference.
BACKGROUND
[0002] The present invention relates to reducing building energy
use.
[0003] Improvements in living condition and advances in health care
have resulted in a marked prolongation of life expectancy for
elderly and disabled population. These individuals, a growing part
of society, are dependent upon the delivery of home health and
general care, which has its own set of challenges and drawbacks.
This population needs continuous general, as well as medical,
supervision and care.
[0004] The bulk of residential energy consumption is devoted to
space heating and cooling. Unlike other end uses, households
typically have direct control over the amount of heating or cooling
used in their home. Unfortunately, energy consumption is typically
reported as a "lump sum" rather than being allocated to specific
devices or end uses. Even advanced metering systems that record
energy use by day, hour, or even minute, only report the aggregate
usage for each household.
[0005] United States Patent Application 20110106471 discloses a
method and system for disaggregating climate control energy use
from non-climate control energy use for a building. The method
includes receiving a series of building energy use values and
corresponding outdoor temperature values for a time period. Each of
the energy use values and outdoor temperature values is associated
with a time interval. The method further includes determining a
series of temperature difference values for the time period based
on a difference in temperature between a predetermined baseline
temperature and each of the outdoor temperature values. A
regression analysis is used to determine a climate control
coefficient and a non-climate control coefficient from the energy
use values and temperature difference values. The climate control
coefficient and/or the non-climate control coefficient are used to
determine climate control energy use and/or non-climate control
energy use for the building.
SUMMARY
[0006] In a first aspect, a system for reducing energy usage
includes one or more utility meters each capturing energy load data
on a fifteen minute data interval or an hourly basis; a usage
disaggregator coupled to one or more of the utility meters to
disaggregate energy consumption for one or more predetermined
appliances based on the data interval (such interval can be 15
minutes interval, 30 minutes interval, or hourly interval, among
others) electrical load signatures of each predetermined appliance;
and an energy messaging module coupled to the energy usage
disaggregator to help users reduce energy consumption.
[0007] In a second aspect, a method to reduce energy usage includes
reading fifteen minute interval or hourly interval energy load data
from utility meters; disaggregating energy consumption for each of
predetermined appliances from the interval of energy load data; and
normatively messaging users to reduce energy consumption.
[0008] In a third aspect, a system for optimizing energy usage
includes one or more utility meters to generate electrical load
data at a 15-minute interval or hourly interval; a load monitoring
disaggregator receiving the interval of electrical load data from
the utility meters to identify power consumption from each of
predetermined appliances; and an energy messaging module coupled to
the disaggregator to generate normative energy saving messages to
users.
[0009] Advantages of the system may include one or more of the
following. Once the system can create accurate energy usage models
for the building and its occupants, the system applies normative
messaging to successfully engage and motivate action across a very
high percentage of targeted individuals. The normative message
motivates office workers to take action which is one of the main
challenges to achieving large scale energy savings. Participation
rates in most energy-efficiency programs are typically less than
5%. By contrast, the messaging system achieves much higher
energy-saving actions by presenting users with only relevant and
immediately actionable suggestions on how to cut down power
consumption in their immediate office/cubicle. The system leverages
behavioral science, customer data analytics, and the latest
software to engage employees of utilities and energy consumers to
collectively take action to save energy. The system enables energy
consumers to increase energy efficiency, reduce costs, and realize
environmental benefits. The system can: [0010] Collect detailed
occupancy/usage data with a combination of sub-meters and low cost
sensors [0011] Create models of occupancy patterns (Daily Office
Activities) [0012] Visualize usage data [0013] Apply occupancy
models with sensor data to automatically control
HVAC/heating/lighting/appliances to save energy [0014] Predict
demand and communicate with utility computers during peak load
[0015] Prompting of building occupants for energy-saving
actions.
BRIEF DESCRIPTION OF THE DRAWINGS
[0016] FIG. 1 illustrates an exemplary embodiment for monitoring
energy usage.
[0017] FIG. 2A shows an exemplary energy consumption for various
appliances over a period of time.
[0018] FIG. 2B shows exemplary signatures for various
appliances.
[0019] FIG. 2C shows exemplary step changes in the power verses
time plot due to individual appliance events.
[0020] FIG. 2D shows an exemplary state transition tables or models
for a two state appliance such as a toaster, a two state appliance
such as a three way lamp, and a refrigerator with a defrost state,
respectively.
[0021] FIG. 3 illustrates a process for classifying daily life
activities.
[0022] FIG. 4 shows an exemplary process to monitor a user.
[0023] FIG. 5 shows an exemplary load disaggregation system working
with hourly data to disaggregate appliance energy usage and to send
messages to prompt users to save energy.
DESCRIPTION
[0024] FIG. 1 shows an exemplary home energy monitoring system. In
this system, a plurality of monitoring cameras 10 are placed in
various predetermined positions in a home of a patient 30. The
cameras 10 can be wired or wireless. For example, the cameras can
communicate over infrared links or over radio links conforming to
the 802.11X (e.g. 802.11A, 802.11B, 802.11G) standard or the
Bluetooth standard to a server 20. The server 20 stores images and
videos of family members or elderly patients 30.
[0025] In one embodiment, electric/gas/water consumption can be
monitored. This approach non-invasively infers user activities
through the operations of appliances during the day. For example,
in the morning, a user may use a toaster and turn on a TV for news.
The user may also turn on lights in the bathrooms and use water for
toiletry and bathing purposes. The user may then turn on a computer
and conduct business using the telephone or cell phone.
Periodically, the user may use the fan or AC or heater as needed.
The user may also use the oven/stove and the kitchen sink for
lunch/dinner preparation. In this brief example, electricity, gas
and water is consumed. The embodiment captures data associated with
electricity, gas and water consumption for modeling user daily
activities, and abnormality in daily activity can be detected
non-invasive manner without requiring the user to wear sensors.
Further, this solution is inexpensive since it can operate off
meters which are installed free by utilities. For example, smart
electric meters uses programmable solid-state meter technology that
provides two-way communication between the meter at the home or
business and the utility, using secure wireless network technology.
The solid-state digital SmartMeter.TM. from PG&E is an electric
meter that records hourly meter reads and periodically transmits
the reads via a dedicated radio frequency (RF) network back to
PG&E. Each SmartMeter.TM. electric meter is equipped with a
network radio, which transmits meter data to an electric network
access point (pictured below). The system uses RF mesh technology,
which allows meters and other sensing devices to securely route
data via nearby meters and relay devices, creating a "mesh" of
network coverage. The system supports two-way communication between
the meter and PG&E. The electric network access point collects
meter data from nearby electric meters and periodically transfers
this data to PG&E via a secure cellular network. Each RF
mesh-enabled device (meters, relays) is connected to several other
mesh-enabled devices, which function as signal repeaters, relaying
the data to an access point. The access point device aggregates,
encrypts, and sends the data back to PG&E over a secure
commercial third-party network. The resulting RF mesh network can
span large distances and reliably transmit data over rough or
difficult terrain. If a meter or other transmitter drops out of the
network, its neighbors find another route. The mesh continually
optimizes routing to ensure information is passed from its source
to its destination as quickly and efficiently as possible.
[0026] The gas system uses point-to-point RF technology to transmit
gas usage data from SmartMeter.TM. gas modules back to PG&E
over a dedicated, secure wireless network. Due to the simpler data
requirements of the gas system, the SmartMeter.TM. gas system
supports only one-way communication from customers to PG&E.
PG&E attaches the SmartMeter.TM. gas module to the traditional
gas meter. This module is outfitted with a radio frequency (RF)
transmitter. The module records daily meter reads and then uses an
RF signal to transmit the reads to a data collector unit (see
below) in the vicinity. The data collector unit (DCU), in turn,
collects meter reads from many meters and securely transmits the
gas usage data over a secure wireless network back to PG&E.
Similarly, water meter can be digitized.
[0027] Various types of information contained in the collected data
can be used to identify a particular activity of life. For example
measurements can be made of: the time the consumption began; the
duration of the consumption; the rate of consumption; the total
amount of utility consumed during a particular period; the maximum
or peak use; the shape and magnitude of the electrical power
waveform (such as the 60 Hz waveform); and any changes in the rate
of consumption. These measurements can be compared to a library of
standard values for different types of loads. The measurements can
also be compared to a library of appliances previously observed on
the utility signal. As an example of how consumption can be used to
identify a load, a toilet flush can be distinguished from a shower
based on the duration of the consumption, the total amount of water
consumed, and the water flow rate.
[0028] Time of day information can sometimes assist in identifying
a life activity. For example water usage in the middle of the night
is more likely to be due to a toilet flush than a shower. Even if
the consumption patterns are not sufficient to completely identify
the loads, they can still be used to help select the most likely
candidates. The user can assist the appliance identification
program by linking an unidentified appliance to the name of an
appliance that is known have been in operation.
[0029] FIG. 2A shows an exemplary energy consumption chart for
various appliances over a period of time while FIG. 2B shows
various exemplary signatures for an electric oven, hair dryer,
water heater and kettle. FIG. 2C shows exemplary step changes in
the power verses time plot due to individual appliance events. FIG.
2D shows an exemplary state transition tables or models for a two
state appliance such as a toaster, a two state appliance such as a
three way lamp, and a refrigerator with a defrost state,
respectively.
[0030] From the signatures, the system can infer daily activity.
When the data is electrical data, additional information may be
measured and used to identify a load. For example, the shape and
size of the 60 Hz conductance waveform (defined as the current
divided by the voltage) may be used to help identify the load.
Typical resistive appliances, such as incandescent lights and
clothes irons, draw current that is in phase with the AC voltage.
Appliances with a reactive and resistive load (such as a DC
transformer for a stereo amplifier and a motor on a clothes washer)
draw current that is out of phase with the voltage. Yet other
appliances, such as computers, have switching power supplies that
consume power for brief intervals during a voltage cycle. Analysis
of the amplitude and temporal variation of the current and power
waveforms can help identify specific loads connected to a circuit.
The circuit can be characterized by its voltage and current
measured at a particular sampling rate, such as 3840 Hz to provide
64 samples per voltage cycle.
[0031] For some loads, the current or voltage may be very stable.
For example, certain light bulbs are either on or off Other loads
may operate at discrete values, such as a ceiling fan with 3
speeds. Further types of loads will have a range of settings, such
as a power drill having variable speed control. Finally, other
loads (such as a refrigerator, TV, or computer) may have more
complex combinations of conductance over time. The system can
include circuits for sampling the electrical power at the
electrical power line and converting the sampled power into digital
format to provide digital signals proportional to circuit load
characteristics such as real power, reactive power, current,
admittance, harmonics, sub-harmonics, dc current,
starting-transient peak; starting-transient duration,
starting-transient time-constant, or starting-transient shape.
Signal processing techniques can be used to analyze the total
household electrical or water use data, into particular daily
activities based on the unique properties of each load. A library
of properties of common loads can be maintained and accessed by the
user interface, user computer, or remote system. For example, the
library can include properties of appliances from model years that
are most likely to be used in the monitored environment.
[0032] When located on the user interface or user computer, this
library can be updated periodically, such as through the internet
by the remote server. Other programming of the user interface, or
software running on the user computer, can also be updated via the
internet, such as with improved algorithms, heuristics, and the
like. In certain implementations, training or other user provided
data is used to update a library that can be shared with other
users. With a broad set of load profiles, the systems will be able
to, in particular examples, automatically identify the loads
consuming the majority of the utilities in the monitored area.
[0033] In some aspects, the systems use a processing algorithm that
employs statistical analysis, such as a least squares fit, to
identify individual loads. In a specific example, an effective
variance analysis is performed on changes in conductance.
Conductance is a useful parameter to characterize the power
consumption behavior of an appliance since it is: (1) voltage
independent (i.e. an appliance's conductance changes minimally with
normal fluctuations in voltage delivered to the circuit) and (2) is
additive for the calculation of power (i.e. the conductance on a
circuit is the sum of the conductances of all appliances).
[0034] In some examples, the voltage and current waveform is
sampled at a sufficient rate such that many data points are
collected for each voltage period. When the AC voltage V passes
from negative to positive, current I and voltage V data points are
each inserted into the first columns of a two dimensional array.
The number of rows in the array is defined by the number of samples
taken during a voltage cycle. When the AC voltage V passes from
negative to positive again, the current and voltage data are
inserted into the next columns of the arrays and so forth. With
this data, instantaneous values of the Power P (I*V) measured in
Watts and Conductance G (I/V) measured in Siemens can be
calculated.
[0035] As noted above, the hardware for non-invasive monitoring is
minimal. In some configurations, meters 120, 130, 140 are directly
connected to the user interface 90, or local computer 20, such as
through a wired connection, including standard communication
protocols and adapters such as RS-232, Ethernet, serial, parallel
port, SPI, SCSI, I2C, ZigBee, and USB connections. In a particular
example, the utility meters 120, 130, 140 send signals to the user
interface 90 over power lines, such as using a power line modem.
The components of the system communicate may use the X10
communication standard. Utility meters 120, 130, and 140 can
generate wireless signals received over the LAN or WAN and then
displayed by user interface 90 or processed on local computer 20.
In some implementations, the user computer accesses the user
interface 90 through a web browser. For example, the user interface
90 may be assigned an internet protocol (IP) address. In particular
examples, the user interface 90 communicates with the user computer
160, remote system 170, or network 180 over the Internet.
[0036] In particular embodiments, adapters can be hooked, mounted
or installed with the meters 120, 130 and 140 by a consumer or
other end users such as a professional electrician or plumber.
Suitable electrical meter adapters can include the Meter Interface
Units (MIUs) available from Archnet of ShenZhen, China. In some
implementations, the electrical adapter for electricity meter 130
can be an in line shunt resistor, a current transducer, or a Hall
Effect sensor. Suitable Hall Effect sensors are available from GMW
Associates of San Carlos, Calif., such as the Sentron CSA-1V. The
water meter adapter can be a photo sensor, such as an infrared or
optical sensor, that detects rotation of a dial mechanism. In one
example, the sensor detects reflection of light off of the dial
mechanism. A light source, such as an optical or infrared LED, is
included, in certain embodiments, to generate a signal to be
measured. An integrated light emitting diode and photodiode is
available from Honeywell (PN# HOA1180). A marker, such as a piece
of more highly light absorbing or reflecting material, may be
placed on the dial in order to help track rotation of the dial. In
further examples, a separate meter, such as a flow meter, is
installed in the gas line or water line. A separate meter may also
be included on the electrical line, such as a voltage or current
meter. In particular implementations, the electrical adapter is
installed between an electrical socket and an existing electrical
meter, such as an electrical meter installed by a power company.
Suitable socket adapters are available from RIOTronics, Inc. of
Englewood, Colo. In some implementations, the adapters read
signals, such as wireless signals, generated by an existing meter,
such as a meter installed by a utility company.
[0037] In some implementations, the electrical adapter, or multiple
electrical adapters, is connected to one or more individual
circuits entering a measurement site. Each circuit may have a
separate adapter, such as an electric metering device, or multiple
circuits may be individually monitored by a single electrical
adapter. In particular examples, the electrical adapter includes a
current transducer (not shown) attached to the wires corresponding
to each breaker switch in a circuit box. A multi-channel analog to
digital voltage sensor may be in communication with the current
transducer to simultaneously monitor multiple circuits.
[0038] The server 20 also executes one or more software modules to
analyze data from the patient. A module 50 monitors the patient's
vital signs such as ECG/EKG and generates warnings should problems
occur. In this module, vital signs can be collected and
communicated to the server 20 using wired or wireless transmitters.
In one embodiment, the server 20 feeds the data to a statistical
analyzer such as a neural network which has been trained to flag
potentially dangerous conditions. The neural network can be a
back-propagation neural network, for example. In this embodiment,
the statistical analyzer is trained with training data where
certain signals are determined to be undesirable for the patient,
given his age, weight, and physical limitations, among others. For
example, the patient's glucose level should be within a
well-established range, and any value outside of this range is
flagged by the statistical analyzer as a dangerous condition. As
used herein, the dangerous condition can be specified as an event
or a pattern that can cause physiological or psychological damage
to the patient. Moreover, interactions between different vital
signals can be accounted for so that the statistical analyzer can
take into consideration instances where individually the vital
signs are acceptable, but in certain combinations, the vital signs
can indicate potentially dangerous conditions. Once trained, the
data received by the server 20 can be appropriately scaled and
processed by the statistical analyzer. In addition to statistical
analyzers, the server 20 can process vital signs using rule-based
inference engines, fuzzy logic, as well as conventional if-then
logic. Additionally, the server can process vital signs using
Hidden Markov Models (HMMs), dynamic time warping, or template
matching, among others.
[0039] A module 52 monitors the patient ambulatory pattern and
generates warnings should the patient's patterns indicate that the
patient has fallen or is likely to fall. 3D detection is used to
monitor the patient's ambulation. In the 3D detection process, by
putting 3 or more known coordinate objects in a scene, camera
origin, view direction and up vector can be calculated and the 3D
space that each camera views can be defined.
[0040] In one embodiment with two or more cameras, camera
parameters (e.g. field of view) are preset to fixed numbers. Each
pixel from each camera maps to a cone space. The system identifies
one or more 3D feature points (such as a birthmark or an
identifiable body landmark) on the patient. The 3D feature point
can be detected by identifying the same point from two or more
different angles. By determining the intersection for the two or
more cones, the system determines the position of the feature
point. The above process can be extended to certain feature curves
and surfaces, e.g. straight lines, arcs; flat surfaces, cylindrical
surfaces. Thus, the system can detect curves if a feature curve is
known as a straight line or arc. Additionally, the system can
detect surfaces if a feature surface is known as a flat or
cylindrical surface. The further the patient is from the camera,
the lower the accuracy of the feature point determination. Also,
the presence of more cameras would lead to more correlation data
for increased accuracy in feature point determination. When
correlated feature points, curves and surfaces are detected, the
remaining surfaces are detected by texture matching and shading
changes. Predetermined constraints are applied based on silhouette
curves from different views. A different constraint can be applied
when one part of the patient is occluded by another object.
Further, as the system knows what basic organic shape it is
detecting, the basic profile can be applied and adjusted in the
process.
[0041] A module 80 communicates with a third party such as the
police department, a security monitoring center, or a call center.
The module 80 operates with a POTS telephone and can use a
broadband medium such as DSL or cable network if available. The
module 80 requires that at least the telephone is available as a
lifeline support. In this embodiment, duplex sound transmission is
done using the POTS telephone network. The broadband network, if
available, is optional for high resolution video and other advanced
services transmission.
[0042] During operation, the module 80 checks whether broadband
network is available. If broadband network is available, the module
80 allows high resolution video, among others, to be broadcasted
directly from the server 20 to the third party or indirectly from
the server 20 to the remote server 200 to the third party. In
parallel, the module 80 allows sound to be transmitted using the
telephone circuit. In this manner, high resolution video can be
transmitted since sound data is separately sent through the POTS
network.
[0043] If broadband network is not available, the system relies on
the POTS telephone network for transmission of voice and images. In
this system, one or more images are compressed for burst
transmission, and at the request of the third party or the remote
server 200, the telephone's sound system is placed on hold for a
brief period to allow transmission of images over the POTS network.
In this manner, existing POTS lifeline telephone can be used to
monitor patients. The resolution and quantity of images are
selectable by the third party. Thus, using only the lifeline as a
communication medium, the person monitoring the patient can elect
to only listen, to view one high resolution image with duplex
telephone voice transmission, to view a few low resolution images,
to view a compressed stream of low resolution video with digitized
voice, among others.
[0044] During installation or while no live person in the scene,
each camera will capture its own environment objects and store it
as background images, the software then detect the live person in
the scene, changes of the live person, so only the portion of live
person will be send to the local server, other compression
techniques will be applied, e.g. send changing file, balanced video
streaming based on change.
[0045] The local server will control and schedule how the
video/picture will be send, e.g. when the camera is view an empty
room, no pictures will be sent, the local server will also
determine which camera is at the right view, and request only the
corresponding video be sent. The local server will determine which
feature it is interested in looking at, e.g. face and request only
that portion be sent.
[0046] With predetermined background images and local server
controlled streaming, the system will enable higher resolution and
more camera system by using narrower bandwidth.
[0047] Through this module, a police officer, a security agent, or
a healthcare agent such as a physician at a remote location can
engage, in interactive visual communication with the patient. The
patient's health data or audio-visual signal can be remotely
accessed. The patient also has access to a video transmission of
the third party. Should the patient experience health symptoms
requiring intervention and immediate care, the health care
practitioner at the central station may summon help from an
emergency services provider. The emergency services provider may
send an ambulance, fire department personnel, family member, or
other emergency personnel to the patient's remote location. The
emergency services provider may, perhaps, be an ambulance facility,
a police station, the local fire department, or any suitable
support facility.
[0048] Communication between the patient's remote location and the
central station can be initiated by a variety of techniques. One
method is by manually or automatically placing a call on the
telephone to the patient's home or from the patient's home to the
central station.
[0049] Alternatively, the system can ask a confirmatory question to
the patient through text to speech software. The patient can be
orally instructed by the health practitioner to conduct specific
physical activities such as specific arm movements, walking,
bending, among others. The examination begins during the initial
conversation with the monitored subject. Any changes in the
spontaneous gestures of the body, arms and hands during speech as
well as the fulfillment of nonspecific tasks are important signs of
possible pathological events. The monitoring person can instruct
the monitored subject to perform a series of simple tasks which can
be used for diagnosis of neurological abnormalities. These
observations may yield early indicators of the onset of a
disease.
[0050] A network 100 such as the Internet receives images from the
server 20 and passes the data to one or more remote servers 200.
The images are transmitted from the server 20 over a secure
communication link such as virtual private network (VPN) to the
remote server(s) 200.
[0051] The server 20 collects data from a plurality of cameras and
uses the 3D images technology to determine if the patient needs
help. The system can transmit video (live or archived) to the
friend, relative, neighbor, or call center for human review. At
each viewer site, after a viewer specifies the correct URL to the
client browser computer, a connection with the server 20 is
established and user identity authenticated using suitable password
or other security mechanisms. The server 200 then retrieves the
document from its local disk or cache memory storage and transmits
the content over the network. In the typical scenario, the user of
a Web browser requests that a media stream file be downloaded, such
as sending, in particular, the URL of a media redirection file from
a Web server. The media redirection file (MRF) is a type of
specialized Hypertext Markup Language (HTML) file that contains
instructions for how to locate the multimedia file and in what
format the multimedia file is in. The Web server returns the MRF
file to the user's browser program. The browser program then reads
the MRF file to determine the location of the media server
containing one or more multimedia content files. The browser then
launches the associated media player application program and passes
the MRF file to it. The media player reads the MRF file to obtain
the information needed to open a connection to a media server, such
as a URL, and the required protocol information, depending upon the
type of medial content is in the file. The streaming media content
file is then routed from the media server down to the user.
[0052] Next, the transactions between the server 20 and one of the
remote servers 200 are detailed. The server 20 compares one image
frame to the next image frame. If no difference exists, the
duplicate frame is deleted to minimize storage space. If a
difference exists, only the difference information is stored as
described in the JPEG standard. This operation effectively
compresses video information so that the camera images can be
transmitted even at telephone modem speed of 64 k or less. More
aggressive compression techniques can be used. For example, patient
movements can be clusterized into a group of known motion vectors,
and patient movements can be described using a set of vectors. Only
the vector data is saved. During view back, each vector is
translated into a picture object which is suitably rasterized. The
information can also be compressed as motion information.
[0053] Next, the server 20 transmits the compressed video to the
remote server 200. The server 200 stores and caches the video data
so that multiple viewers can view the images at once since the
server 200 is connected to a network link such as telephone line
modem, cable modem, DSL modem, and ATM transceiver, among
others.
[0054] In one implementation, the servers 200 use RAID-5 striping
and parity techniques to organize data in a fault tolerant and
efficient manner. The RAID (Redundant Array of Inexpensive Disks)
approach is well described in the literature and has various levels
of operation, including RAID-5, and the data organization can
achieve data storage in a fault tolerant and load balanced manner.
RAID-5 provides that the stored data is spread among three or more
disk drives, in a redundant manner, so that even if one of the disk
drives fails, the data stored on the drive can be recovered in an
efficient and error free manner from the other storage locations.
This method also advantageously makes use of each of the disk
drives in relatively equal and substantially parallel operations.
Accordingly, if one has a six gigabyte cluster volume which spans
three disk drives, each disk drive would be responsible for
servicing two gigabytes of the cluster volume. Each two gigabyte
drive would be comprised of one-third redundant information, to
provide the redundant, and thus fault tolerant, operation required
for the RAID-5 approach. For additional physical security, the
server can be stored in a Fire Safe or other secured box, so there
is no chance to erase the recorded data, this is very important for
forensic analysis.
[0055] The system can also monitor the patient's gait pattern and
generate warnings should the patient's gait patterns indicate that
the patient is likely to fall. The system will detect patient
skeleton structure, stride and frequency; and based on this
information to judge whether patient has joint problem,
asymmetrical bone structure, among others. The system can store
historical gait information, and by overlaying current structure to
the historical (normal) gait information, gait changes can be
detected.
[0056] The system also provides a patient interface 90 to assist
the patient in easily accessing information. In one embodiment, the
patient interface includes a touch screen; voice-activated text
reading; one touch telephone dialing; and video conferencing. The
touch screen has large icons that are pre-selected to the patient's
needs, such as his or her favorite web sites or application
programs. The voice activated text reading allows a user with poor
eye-sight to get information from the patient interface 90. Buttons
with pre-designated dialing numbers, or video conferencing contact
information allow the user to call a friend or a healthcare
provider quickly.
[0057] In one embodiment, medicine for the patient is tracked using
radio frequency identification (RFID) tags. In this embodiment,
each drug container is tracked through an RFID tag that is also a
drug label. The RF tag is an integrated circuit that is coupled
with a mini-antenna to transmit data. The circuit contains memory
that stores the identification Code and other pertinent data to be
transmitted when the chip is activated or interrogated using radio
energy from a reader. A reader consists of an RF antenna,
transceiver and a micro-processor. The transceiver sends activation
signals to and receives identification data from the tag. The
antenna may be enclosed with the reader or located outside the
reader as a separate piece. RFID readers communicate directly with
the RFID tags and send encrypted usage data over the patient's
network to the server 20 and eventually over the Internet 100. The
readers can be built directly into the walls or the cabinet
doors.
[0058] In one embodiment, capacitively coupled RFID tags are used.
The capacitive RFID tag includes a silicon microprocessor that can
store 96 bits of information, including the pharmaceutical
manufacturer, drug name, usage instruction and a 40-bit serial
number. A conductive carbon ink acts as the tag's antenna and is
applied to a paper substrate through conventional printing means.
The silicon chip is attached to printed carbon-ink electrodes on
the back of a paper label, creating a low-cost, disposable tag that
can be integrated on the drug label. The information stored on the
drug labels is written in a Medicine Markup Language (MML), which
is based on the eXtensible Markup Language (XML). MML would allow
all computers to communicate with any computer system in a similar
way that Web servers read Hyper Text Markup Language (HTML), the
common language used to create Web pages.
[0059] After receiving the medicine container, the patient places
the medicine in a medicine cabinet, which is also equipped with a
tag reader. This smart cabinet then tracks all medicine stored in
it. It can track the medicine taken, how often the medicine is
restocked and can let the patient know when a particular medication
is about to expire. At this point, the server 20 can order these
items automatically. The server 20 also monitors drug compliance,
and if the patient does not remove the bottle to dispense
medication as prescribed, the server 20 sends a warning to the
healthcare provider.
[0060] FIG. 3 shows an exemplary process to non-invasively infer
daily life activities. Although the detection needs not be done in
any particular order, an exemplary sequence is discussed. In one
implementation, the process detects wake-up time in the daily
activity patterns in 201. For example, the wake-up time may be
detected by detecting existence of a person on a bed by using a
pyroelectric infrared sensor, detecting that a television set is
turned on in the morning or detecting electrical activities in the
restroom in the morning. In another example, the bedtime may be
detected by detecting that the television set is turned off at
night or detecting the electric lamp being turned off in the
bedroom. A telephone time detection 202 detects calling time in the
daily activity patterns by receiving and analyzing phone bills or
alternatively each time a phone is used, the phone transmits a log
to the monitoring server to indicate the time when the person to be
observed is using the phone. A toilet time detection 203 detects
toilet-using time in the daily activity patterns by detecting that
the electric lamp in the toilet is turned on/off and a low volume
of water consumption rate. An entertainment time detection 204
detects TV watching time in the daily activity patterns by
receiving and analyzing TV display power consumption or
alternatively when stereo equipment is on. A bathing time detection
205 detects bathing time in the daily activity patterns by
detecting a high volume of water consumption rate and that the
electric lamp in the bathroom is turned on. A cooking time
detection 206 detects cooking time in the daily activity patterns
and is comprised of one or more sensors or one or more home
electric appliances for detecting the time when the person to be
observed is cooking. For example, the cooking time may be detected
by detecting that a rice cooker or microwave oven is turned on/off,
detecting that a gas range or an IH (Induction-Heating) cooking
heater is turned on/off or detecting other cooking home electric
appliances are turned on/off.
[0061] A room-to-room movement frequency detection 207 detects the
number of movement between rooms in the daily activity patterns and
is comprised of one or more sensors or one or more home electric
appliances for detecting the number of movement between the rooms.
For example, the number of movement between the rooms may be
detected by detecting that the electric lamps in each room are
turned on/off or detecting that other home electric appliances in
each room are turned on/off.
[0062] Data of the daily activity patterns is detected by these
detection sensors and transmitted to the data processing apparatus
in a wireless or wired manner and, then, the transmitted data is
stored in databases of the data processing apparatus. Every time
the data processing apparatus receives the data of the daily
activity patterns from the detection sensors, it performs the
statistical analyses of the stored data so as to determine whether
the received daily activity pattern is abnormal or not. If it is
determined that the received daily activity pattern is abnormal,
the reporting apparatus in the home of the person to be observed or
the reporting apparatuses are informed of the abnormality. In
response to the abnormality notification, the person to be observed
or the observers checks whether the abnormality notification is
correct or not and gives the data processing apparatus feedback
about whether the abnormality notification is correct or not. Based
on the feedback information, the data processing apparatus
determines whether the daily activity patterns that have been
considered abnormal correspond to the actual abnormalities or not
and learns the daily activity patterns unique to the person to be
observed. Here, although examples of the sensors for detecting the
daily activity patterns include only the wake-up time detection,
the bedtime detection, the toilet time detection, the room cleaning
time detection, the bathing time detection, the cooking time
detection and the room-to-room movement frequency detection as
described above, other sensors for detecting the daily activity
patterns may be provided.
[0063] For example, if the user typically sleeps between 10 pm to 6
am, the location would reflect that the user's location maps to the
bedroom between 10 pm and 6 am. In one exemplary system with an
optional heart rate monitor, the system builds a schedule of the
user's activity as follows:
TABLE-US-00001 Location Time Start Time End Heart Rate Bed room 10
pm 6 am 60-80 Gym room 6 am 7 am 90-120 Bath room 7 am 7:30 am
85-120 Dining room 7:30 am 8:45 am 80-90 Home Office 8:45 am 11:30
am 85-100 . . . . . .
[0064] FIG. 4 shows an exemplary process to monitor a patient.
First, the process acquires utility meter data (304). In one
embodiment, a direct data connection to a utility company database
can be done. In another embodiment, sensors can be placed next to
utility meters to get the data without having to get data from the
utility company. Next, the process identifies individual appliance
utility consumption from the utility meter data (306). The process
then determines daily life activity patterns from the individual
appliance utility consumption; and sending a request for assistance
when the pattern matches one or more predetermined conditions
(310).
[0065] The predetermined conditions can be dangerous conditions
such as when a person has fallen, as detected by the 3D
accelerometers, or indirectly such as when the patient is in the
bathroom for an unusual period. The dangerous condition can include
being in one position (such as bed or chair) for too long; having
an oven on for an extended period, having the TV on without turning
on lights in the bed room past a normal sleep time, or may be as
simple as the cellphone being turned off for too long. The
predetermined conditions can be programmed by a system installer,
and may not relate to dangerous conditions, but simply conditions
where someone such as a family member or a caretaker should follow
up to ensure patient safety.
[0066] In one embodiment, the phone can simply request that the
user shuts off an alarm countdown or acknowledge that the patient
is doing ok to prevent false alarms. The daily life activity
tracking is adaptive in that it gradually adjusts to the user's new
activities and/or habits. If there are sudden changes, the system
flags these sudden changes for follow up. For instance, if the user
spends three hours in the bathroom, the system prompts the third
party (such as a call center) to follow up with the patient to make
sure he or she does not need help.
[0067] In one embodiment, data driven analyzers may be used to
track the patient's habits. These data driven analyzers may
incorporate a number of models such as parametric statistical
models, non-parametric statistical models, clustering models,
nearest neighbor models, regression methods, and engineered
(artificial) neural networks. Prior to operation, data driven
analyzers or models of the patient's habits or ambulation patterns
are built using one or more training sessions. The data used to
build the analyzer or model in these sessions are typically
referred to as training data. As data driven analyzers are
developed by examining only training examples, the selection of the
training data can significantly affect the accuracy and the
learning speed of the data driven analyzer. One approach used
heretofore generates a separate data set referred to as a test set
for training purposes. The test set is used to avoid overfitting
the model or analyzer to the training data. Overfitting refers to
the situation where the analyzer has memorized the training data so
well that it fails to fit or categorize unseen data. Typically,
during the construction of the analyzer or model, the analyzer's
performance is tested against the test set. The selection of the
analyzer or model parameters is performed iteratively until the
performance of the analyzer in classifying the test set reaches an
optimal point. At this point, the training process is completed. An
alternative to using an independent training and test set is to use
a methodology called cross-validation. Cross-validation can be used
to determine parameter values for a parametric analyzer or model
for a non-parametric analyzer. In cross-validation, a single
training data set is selected. Next, a number of different
analyzers or models are built by presenting different parts of the
training data as test sets to the analyzers in an iterative
process. The parameter or model structure is then determined on the
basis of the combined performance of all models or analyzers. Under
the cross-validation approach, the analyzer or model is typically
retrained with data using the determined optimal model
structure.
[0068] In general, multiple dimensions of a user's daily activities
such as start and stop times of interactions of different
interactions are encoded as distinct dimensions in a database. A
predictive model, including time series models such as those
employing autoregression analysis and other standard time series
methods, dynamic Bayesian networks and Continuous Time Bayesian
Networks, or temporal Bayesian-network representation and reasoning
methodology, is built, and then the model, in conjunction with a
specific query makes target inferences.
[0069] Bayesian networks provide not only a graphical, easily
interpretable alternative language for expressing background
knowledge, but they also provide an inference mechanism; that is,
the probability of arbitrary events can be calculated from the
model. Intuitively, given a Bayesian network, the task of mining
interesting unexpected patterns can be rephrased as discovering
item sets in the data which are much more--or much less--frequent
than the background knowledge suggests. These cases are provided to
a learning and inference subsystem, which constructs a Bayesian
network that is tailored for a target prediction. The Bayesian
network is used to build a cumulative distribution over events of
interest.
[0070] In another embodiment, a genetic algorithm (GA) search
technique can be used to find approximate solutions to identifying
the user's habits. Genetic algorithms are a particular class of
evolutionary algorithms that use techniques inspired by
evolutionary biology such as inheritance, mutation, natural
selection, and recombination (or crossover). Genetic algorithms are
typically implemented as a computer simulation in which a
population of abstract representations (called chromosomes) of
candidate solutions (called individuals) to an optimization problem
evolves toward better solutions. Traditionally, solutions are
represented in binary as strings of 0s and 1s, but different
encodings are also possible. The evolution starts from a population
of completely random individuals and happens in generations. In
each generation, the fitness of the whole population is evaluated,
multiple individuals are stochastically selected from the current
population (based on their fitness), modified (mutated or
recombined) to form a new population, which becomes current in the
next iteration of the algorithm.
[0071] Substantially any type of learning system or process may be
employed to determine the user's ambulatory and living patterns so
that unusual events can be flagged.
[0072] In one embodiment, clustering operations are performed to
detect patterns in the data. In another embodiment, a neural
network is used to recognize each pattern as the neural network is
quite robust at recognizing user habits or patterns. Once the
treatment features have been characterized, the neural network then
compares the input user information with stored templates of
treatment vocabulary known by the neural network recognizer, among
others. The recognition models can include a Hidden Markov Model
(HMM), a dynamic programming model, a neural network, a fuzzy
logic, or a template matcher, among others. These models may be
used singly or in combination.
[0073] Dynamic programming considers all possible points within the
permitted domain for each value of i. Because the best path from
the current point to the next point is independent of what happens
beyond that point. Thus, the total cost of [i(k), j(k)] is the cost
of the point itself plus the cost of the minimum path to it.
Preferably, the values of the predecessors can be kept in an
M.times.N array, and the accumulated cost kept in a 2.times.N array
to contain the accumulated costs of the immediately preceding
column and the current column. However, this method requires
significant computing resources. For the recognizer to find the
optimal time alignment between a sequence of frames and a sequence
of node models, it must compare most frames against a plurality of
node models. One method of reducing the amount of computation
required for dynamic programming is to use pruning Pruning
terminates the dynamic programming of a given portion of user habit
information against a given treatment model if the partial
probability score for that comparison drops below a given
threshold. This greatly reduces computation.
[0074] Considered to be a generalization of dynamic programming, a
hidden Markov model is used in the preferred embodiment to evaluate
the probability of occurrence of a sequence of observations O(1),
O(2), . . . O(t), . . . , O(T), where each observation O(t) may be
either a discrete symbol under the VQ approach or a continuous
vector. The sequence of observations may be modeled as a
probabilistic function of an underlying Markov chain having state
transitions that are not directly observable. In one embodiment,
the Markov network is used to model a number of user habits and
activities. The transitions between states are represented by a
transition matrix A=[a(i,j)]. Each a(i,j) term of the transition
matrix is the probability of making a transition to state j given
that the model is in state i. The output symbol probability of the
model is represented by a set of functions B=[b(j) (O(t)], where
the b(j) (O(t) term of the output symbol matrix is the probability
of outputting observation O(t), given that the model is in state j.
The first state is always constrained to be the initial state for
the first time frame of the utterance, as only a prescribed set of
left to right state transitions are possible. A predetermined final
state is defined from which transitions to other states cannot
occur. Transitions are restricted to reentry of a state or entry to
one of the next two states. Such transitions are defined in the
model as transition probabilities. Although the preferred
embodiment restricts the flow graphs to the present state or to the
next two states, one skilled in the art can build an HMM model
without any transition restrictions, although the sum of all the
probabilities of transitioning from any state must still add up to
one. In each state of the model, the current feature frame may be
identified with one of a set of predefined output symbols or may be
labeled probabilistically. In this case, the output symbol
probability b(j) O(t) corresponds to the probability assigned by
the model that the feature frame symbol is O(t). The model
arrangement is a matrix A=[a(i,j)] of transition probabilities and
a technique of computing B=b(j) O(t), the feature frame symbol
probability in state j. The Markov model is formed for a reference
pattern from a plurality of sequences of training patterns and the
output symbol probabilities are multivariate Gaussian function
probability densities. The patient habit information is processed
by a feature extractor. During learning, the resulting feature
vector series is processed by a parameter estimator, whose output
is provided to the hidden Markov model. The hidden Markov model is
used to derive a set of reference pattern templates, each template
representative of an identified pattern in a vocabulary set of
reference treatment patterns. The Markov model reference templates
are next utilized to classify a sequence of observations into one
of the reference patterns based on the probability of generating
the observations from each Markov model reference pattern template.
During recognition, the unknown pattern can then be identified as
the reference pattern with the highest probability in the
likelihood calculator. The HMM template has a number of states,
each having a discrete value. However, because treatment pattern
features may have a dynamic pattern in contrast to a single value.
The addition of a neural network at the front end of the HMM in an
embodiment provides the capability of representing states with
dynamic values. The input layer of the neural network comprises
input neurons. The outputs of the input layer are distributed to
all neurons in the middle layer. Similarly, the outputs of the
middle layer are distributed to all output states, which normally
would be the output layer of the neuron. However, each output has
transition probabilities to itself or to the next outputs, thus
forming a modified HMM. Each state of the thus formed HMM is
capable of responding to a particular dynamic signal, resulting in
a more robust HMM. Alternatively, the neural network can be used
alone without resorting to the transition probabilities of the HMM
architecture.
[0075] The system allows patients to conduct a low-cost,
comprehensive, real-time monitoring of their vital daily life
activities. Information can be viewed using an Internet-based
website, a personal computer, or simply by viewing a display on the
monitor. Data measured several times each day provide a relatively
comprehensive data set compared to that measured during medical
appointments separated by several weeks or even months. This allows
both the patient and medical professional to observe trends in the
data, such as a gradual increase or decrease in blood pressure,
which may indicate a medical condition. The invention also
minimizes effects of white coat syndrome since the monitor
automatically makes measurements with basically no discomfort;
measurements are made at the patient's home or work, rather than in
a medical office.
[0076] To view information on daily life activities, the patient or
an authorized third party such as family members, emergency
personnel, or medical professional accesses a patient user
interface hosted on the web server 200 through the Internet 100
from a remote computer system. The patient interface displays vital
information such as ambulation, blood pressure and related data
measured from a single patient. The system may also include a call
center, typically staffed with medical professionals such as
doctors, nurses, or nurse practioners, whom access a care-provider
interface hosted on the same website on the server 200. The
care-provider interface displays vital data from multiple
patients.
[0077] The wearable appliance has an indoor positioning system and
processes these signals to determine a location (e.g., latitude,
longitude, and altitude) of the monitor and, presumably, the
patient. This location could be plotted on a map by the server, and
used to locate a patient during an emergency, e.g. to dispatch an
ambulance.
[0078] In one embodiment, the web page hosted by the server 200
includes a header field that lists general information about the
patient (e.g. name, age, and ID number, general location, and
information concerning recent measurements); a table that lists
recently measured blood pressure data and suggested (i.e.
doctor-recommended) values of these data; and graphs that plot the
systolic and diastolic blood pressure data in a time-dependent
manner. The header field additionally includes a series of tabs
that each link to separate web pages that include, e.g., tables and
graphs corresponding to a different data measured by the wearable
device such as calorie consumption/dissipation, ambulation pattern,
sleeping pattern, heart rate, pulse oximetry, and temperature. The
table lists a series of data fields that show running average
values of the patient's daily, monthly, and yearly vital
parameters. The levels are compared to a series of corresponding
`suggested` values of vital parameters that are extracted from a
database associated with the web site. The suggested values depend
on, among other things, the patient's age, sex, and weight. The
table then calculates the difference between the running average
and suggested values to give the patient an idea of how their data
compares to that of a healthy patient. The web software interface
may also include security measures such as authentication,
authorization, encryption, credential presentation, and digital
signature resolution. The interface may also be modified to conform
to industry-mandated, XML schema definitions, while being
`backwards compatible` with any existing XML schema
definitions.
[0079] The system provides for self-registration of Internet
enabled appliances by the user. Data can be synchronized between
the Repository and appliance(s) via the base station 20. The user
can preview the readings received from the appliance(s) and reject
erroneous readings. The user or treating professional can set up
the system to generate alerts against received data, based on
pre-defined parameters. The system can determine trends in received
data, based on user defined parameters.
[0080] Appliance registration is the process by which a patient
monitoring appliance is associated with one or more users of the
system. This mechanism is also used when provisioning appliances
for a user by a third party, such as a clinician (or their
respective delegate). In one implementation, the user (or delegate)
logs into the portal to select one or more appliances and available
for registration. In turn, the base station server 20 broadcasts a
query to all nodes in the mesh network to retrieve identification
information for the appliance such as manufacturer information,
appliance model information, appliance serial number and optionally
a hub number (available on hub packaging). The user may register
more than one appliance at this point. The system optionally sets
up a service subscription for appliance(s) usage. This includes
selecting service plans and providing payment information. The
appliance(s) are then associated with this user's account and a
control file with appliance identification information is
synchronized between the server 200 and the base station 20 and
each appliance on initialization. In one embodiment, each appliance
8 transmits data to the base station 20 in an XML format for ease
of interfacing and is either kept encrypted or in a non-readable
format on the base station 20 for security reasons.
[0081] The base station 20 frequently collects and synchronizes
data from the appliances 8. The base station 20 may use one of
various transportation methods to connect to the repository on the
server 200 using a PC as conduit or through a connection
established using an embedded modem (connected to a phone line), a
wireless router (DSL or cable wireless router), a cellular modem,
or another network-connected appliance (such as, but not limited
to, a web-phone, video-phone, embedded computer, PDA or handheld
computer).
[0082] In one embodiment, users may set up alerts or reminders that
are triggered when one or more reading meet a certain set of
conditions, depending on parameters defined by the user. The user
chooses the condition that they would like to be alerted to and by
providing the parameters (e.g. threshold value for the reading) for
alert generation. Each alert may have an interval which may be
either the number of data points or a time duration in units such
as hours, days, weeks or months. The user chooses the destination
where the alert may be sent. This destination may include the
user's portal, e-mail, pager, voice-mail or any combination of the
above.
[0083] Trends are determined by applying mathematical and
statistical rules (e.g. moving average and deviation) over a set of
reading values. Each rule is configurable by parameters that are
either automatically calculated or are set by the user.
[0084] The user may give permission to others as needed to read or
edit their personal data or receive alerts. The user or clinician
could have a list of people that they want to monitor and have it
show on their "My Account" page, which serves as a local central
monitoring station in one embodiment. Each person may be assigned
different access rights which may be more or less than the access
rights that the patient has. For example, a doctor or clinician
could be allowed to edit data for example to annotate it, while the
patient would have read-only privileges for certain pages. An
authorized person could set the reminders and alerts parameters
with limited access to others. In one embodiment, the base station
server 20 serves a web page customized by the user or the user's
representative as the monitoring center that third parties such as
family, physicians, or caregivers can log in and access
information. In another embodiment, the base station 20
communicates with the server 200 at a call center so that the call
center provides all services. In yet another embodiment, a hybrid
solution where authorized representatives can log in to the base
station server 20 access patient information while the call center
logs into both the server 200 and the base station server 20 to
provide complete care services to the patient.
[0085] The server 200 may communicate with a business process
outsourcing (BPO) company or a call center to provide central
monitoring in an environment where a small number of monitoring
agents can cost effectively monitor multiple people 24 hours a day.
A call center agent, a clinician or a nursing home manager may
monitor a group or a number of users via a summary "dashboard" of
their readings data, with ability to drill-down into details for
the collected data. A clinician administrator may monitor the data
for and otherwise administer a number of users of the system. A
summary "dashboard" of readings from all Patients assigned to the
Administrator is displayed upon log in to the Portal by the
Administrator. Readings may be color coded to visually distinguish
normal vs. readings that have generated an alert, along with
description of the alert generated. The Administrator may drill
down into the details for each Patient to further examine the
readings data, view charts etc. in a manner similar to the
Patient's own use of the system. The Administrator may also view a
summary of all the appliances registered to all assigned Patients,
including but not limited to all appliance identification
information. The Administrator has access only to information about
Patients that have been assigned to the Administrator by a Super
Administrator. This allows for segmenting the entire population of
monitored Patients amongst multiple Administrators. The Super
Administrator may assign, remove and/or reassign Patients amongst a
number of Administrators.
[0086] In one embodiment, a patient using an Internet-accessible
computer and web browser, directs the browser to an appropriate URL
and signs up for a service for a short-term (e.g., 1 month) period
of time. The company providing the service completes an
accompanying financial transaction (e.g. processes a credit card),
registers the patient, and ships the patient a wearable appliance
for the short period of time. The registration process involves
recording the patient's name and contact information, a number
associated with the monitor (e.g. a serial number), and setting up
a personalized website. The patient then uses the monitor
throughout the monitoring period, e.g. while working, sleeping, and
exercising. During this time the monitor measures data from the
patient and wirelessly transmits it through the channel to a data
center. There, the data are analyzed using software running on
computer servers to generate a statistical report. The computer
servers then automatically send the report to the patient using
email, regular mail, or a facsimile machine at different times
during the monitoring period. When the monitoring period is
expired, the patient ships the wearable appliance back to the
monitoring company.
[0087] Different web pages may be designed and accessed depending
on the end-user. As described above, individual users have access
to web pages that only their ambulation and blood pressure data
(i.e., the patient interface), while organizations that support a
large number of patients (nursing homes or hospitals) have access
to web pages that contain data from a group of patients using a
care-provider interface. Other interfaces can also be used with the
web site, such as interfaces used for: insurance companies, members
of a particular company, clinical trials for pharmaceutical
companies, and e-commerce purposes. Vital patient data displayed on
these web pages, for example, can be sorted and analyzed depending
on the patient's medical history, age, sex, medical condition, and
geographic location. The web pages also support a wide range of
algorithms that can be used to analyze data once they are extracted
from the data packets. For example, an instant message or email can
be sent out as an `alert` in response to blood pressure indicating
a medical condition that requires immediate attention.
Alternatively, the message could be sent out when a data parameter
(e.g. systolic blood pressure) exceeds a predetermined value. In
some cases, multiple parameters (e.g., fall detection, positioning
data, and blood pressure) can be analyzed simultaneously to
generate an alert message. In general, an alert message can be sent
out after analyzing one or more data parameters using any type of
algorithm. These algorithms range from the relatively simple (e.g.,
comparing blood pressure to a recommended value) to the complex
(e.g., predictive medical diagnoses using `data mining`
techniques). In some cases data may be `fit` using algorithms such
as a linear or non-linear least-squares fitting algorithm.
[0088] In one embodiment, a physician, other health care
practitioner, or emergency personnel is provided with access to
patient medical information through the server 200. In one
embodiment, if the wearable appliance detects that the patient
needs help, or if the patient decides help is needed, the system
can call his or her primary care physician. If the patient is
unable to access his or her primary care physician (or another
practicing physician providing care to the patient) a call from the
patient is received, by an answering service or a call center
associated with the patient or with the practicing physician. The
call center determines whether the patient is exhibiting symptoms
of an emergency condition by polling vital patient information
generated by the wearable device, and if so, the answering service
contacts 911 emergency service or some other emergency service. The
call center can review falls information, blood pressure
information, and other vital information to determine if the
patient is in need of emergency assistance. If it is determined
that the patient in not exhibiting symptoms of an emergent
condition, the answering service may then determine if the patient
is exhibiting symptoms of a non-urgent condition. If the patient is
exhibiting symptoms of a non-urgent condition, the answering
service will inform the patient that he or she may log into the
server 200 for immediate information on treatment of the condition.
If the answering service determines that the patient is exhibiting
symptoms that are not related to a non-urgent condition, the
answering service may refer the patient to an emergency room, a
clinic, the practicing physician (when the practicing physician is
available) for treatment.
[0089] In another embodiment, the wearable appliance permits direct
access to the call center when the user pushes a switch or button
on the appliance, for instance. In one implementation, telephones
and switching systems in call centers are integrated with the home
mesh network to provide for, among other things, better routing of
telephone calls, faster delivery of telephone calls and associated
information, and improved service with regard to client
satisfaction through computer-telephony integration (CTI). CTI
implementations of various design and purpose are implemented both
within individual call-centers and, in some cases, at the telephone
network level. For example, processors running CTI software
applications may be linked to telephone switches, service control
points (SCPs), and network entry points within a public or private
telephone network. At the call-center level, CTI-enhanced
processors, data servers, transaction servers, and the like, are
linked to telephone switches and, in some cases, to similar CTI
hardware at the network level, often by a dedicated digital link.
CTI processors and other hardware within a call-center is commonly
referred to as customer premises equipment (CPE). It is the CTI
processor and application software is such centers that provides
computer enhancement to a call center. In a CTI-enhanced call
center, telephones at agent stations are connected to a central
telephony switching apparatus, such as an automatic call
distributor (ACD) switch or a private branch exchange (PBX). The
agent stations may also be equipped with computer terminals such as
personal computer/video display unit's (PC/VDU's) so that agents
manning such stations may have access to stored data as well as
being linked to incoming callers by telephone equipment. Such
stations may be interconnected through the PC/VDUs by a local area
network (LAN). One or more data or transaction servers may also be
connected to the LAN that interconnects agent stations. The LAN is,
in turn, typically connected to the CTI processor, which is
connected to the call switching apparatus of the call center.
[0090] When a call from a patient arrives at a call center, whether
or not the call has been pre-processed at an SCP, the telephone
number of the calling line and the medical record are made
available to the receiving switch at the call center by the network
provider. This service is available by most networks as caller-ID
information in one of several formats such as Automatic Number
Identification (ANI). Typically the number called is also available
through a service such as Dialed Number Identification Service
(DNIS). If the call center is computer-enhanced (CTI), the phone
number of the calling party may be used as a key to access
additional medical and/or historical information from a customer
information system (CIS) database at a server on the network that
connects the agent workstations. In this manner information
pertinent to a call may be provided to an agent, often as a screen
pop on the agent's PC/VDU.
[0091] The call center enables any of a first plurality of
physician or health care practitioner terminals to be in audio
communication over the network with any of a second plurality of
patient wearable appliances. The call center will route the call to
a physician or other health care practitioner at a physician or
health care practitioner terminal and information related to the
patient (such as an electronic medical record) will be received at
the physician or health care practitioner terminal via the network.
The information may be forwarded via a computer or database in the
practicing physician's office or by a computer or database
associated with the practicing physician, a health care management
system or other health care facility or an insurance provider. The
physician or health care practitioner is then permitted to assess
the patient, to treat the patient accordingly, and to forward
updated information related to the patient (such as examination,
treatment and prescription details related to the patient's visit
to the patient terminal) to the practicing physician via the
network 200.
[0092] In one embodiment, the system informs a patient of a
practicing physician of the availability of the web services and
referring the patient to the web site upon agreement of the
patient. A call from the patient is received at a call center. The
call center enables physicians to be in audio communication over
the network with any patient wearable appliances, and the call is
routed to an available physician at one of the physician so that
the available physician may carry on a two-way conversation with
the patient. The available physician is permitted to make an
assessment of the patient and to treat the patient. The system can
forward information related to the patient to a health care
management system associated with the physician. The health care
management system may be a healthcare management organization, a
point of service health care system, or a preferred provider
organization. The health care practitioner may be a nurse
practitioner or an internist.
[0093] The available health care practitioner can make an
assessment of the patient and to conduct an examination of the
patient over the network, including optionally by a visual study of
the patient. The system can make an assessment in accordance with a
protocol. The assessment can be made in accordance with a protocol
stored in a database and/or making an assessment in accordance with
the protocol may include displaying in real time a relevant segment
of the protocol to the available physician. Similarly, permitting
the physician to prescribe a treatment may include permitting the
physician to refer the patient to a third party for treatment
and/or referring the patient to a third party for treatment may
include referring the patient to one or more of a primary care
physician, specialist, hospital, emergency room, ambulance service
or clinic. Referring the patient to a third party may additionally
include communicating with the third party via an electronic link
included in a relevant segment of a protocol stored in a protocol
database resident on a digital storage medium and the electronic
link may be a hypertext link. When a treatment is being prescribed
by a physician, the system can communicate a prescription over the
network to a pharmacy and/or communicating the prescription over
the network to the pharmacy may include communicating to the
pharmacy instructions to be given to the patient pertaining to the
treatment of the patient. Communicating the prescription over the
network to the pharmacy may also include communicating the
prescription to the pharmacy via a hypertext link included in a
relevant segment of a protocol stored in a database resident on a
digital storage medium. In accordance with another related
embodiment, permitting the physician to conduct the examination may
be accomplished under conditions such that the examination is
conducted without medical instruments at the patient terminal where
the patient is located.
[0094] In another embodiment, a system for delivering medical
examination, diagnosis, and treatment services from a physician to
a patient over a network includes a first plurality of health care
practitioners at a plurality of terminals, each of the first
plurality of health care practitioner terminals including a display
device that shows information collected by the wearable appliances
and a second plurality of patient terminals or wearable appliances
in audiovisual communication over a network with any of the first
plurality of health care practitioner terminals. A call center is
in communication with the patient wearable appliances and the
health care practitioner terminals, the call center routing a call
from a patient at one of the patient terminals to an available
health care practitioner at one of the health care practitioner
terminals, so that the available health care practitioner may carry
on a two-way conversation with the patient. A protocol database
resident on a digital storage medium is accessible to each of the
health care practitioner terminals. The protocol database contains
a plurality of protocol segments such that a relevant segment of
the protocol may be displayed in real time on the display device of
the health care practitioner terminal of the available health care
practitioner for use by the available health care practitioner in
making an assessment of the patient. The relevant segment of the
protocol displayed in real time on the display device of the health
care practitioner terminal may include an electronic link that
establishes communication between the available health care
practitioner and a third party and the third party may be one or
more of a primary care physician, specialist, hospital, emergency
room, ambulance service, clinic or pharmacy.
[0095] In accordance with other related embodiment, the patient
wearable appliance may include establish a direct connection to the
call center by pushing a button on the appliance. Further, the
protocol database may be resident on a server that is in
communication with each of the health care practitioner terminals
and each of the health care practitioner terminals may include a
local storage device and the protocol database is replicated on the
local storage device of one or more of the physician terminals.
[0096] In another embodiment, a system for delivering medical
examination, diagnosis, and treatment services from a physician to
a patient over a network includes a first plurality of health care
practitioner terminals, each of the first plurality of health care
practitioner terminals including a display device and a second
plurality of patient terminals in audiovisual communication over a
network with any of the first plurality of health care practitioner
terminals. Each of the second plurality of patient terminals
includes a camera having pan, tilt and zoom modes, such modes being
controlled from the first plurality of health care practitioner
terminals. A call center is in communication with the patient
terminals and the health care practitioner terminals and the call
center routes a call from a patient at one of the patient terminals
to an available health care practitioner at one of the health care
practitioner terminals, so that the available health care
practitioner may carry on a two-way conversation with the patient
and visually observe the patient.
[0097] In one embodiment, the information is store in a secure
environment, with security levels equal to those of online banking,
social security number input, and other confidential information.
Conforming to Health Insurance Portability and Accountability Act
(HIPAA) requirements, the system creates audit trails, requires
logins and passwords, and provides data encryption to ensure the
patient information is private and secure. The HIPAA privacy
regulations ensure a national floor of privacy protections for
patients by limiting the ways that health plans, pharmacies,
hospitals and other covered entities can use patients' personal
medical information. The regulations protect medical records and
other individually identifiable health information, whether it is
on paper, in computers or communicated orally.
[0098] FIG. 5 shows an exemplary process to use NILM with hourly
data. First, the process reads hourly energy load data from utility
meters (400). Then the NILM engine disaggregates energy consumption
for each of predetermined appliances from the hourly energy load
data (410). Once the energy consumption has been disaggregated to
show appliance energy usage data, the system can send normatively
messages to users to reduce energy consumption (420).
[0099] In one embodiment, once the system has accurate energy usage
models for the building and its occupants, the system applies
normative messaging to successfully engage and motivate action
across a very high percentage of targeted individuals. The
normative message motivates office workers to take action which is
one of the main challenges to achieving large scale energy savings.
Participation rates in most energy-efficiency programs are
typically less than 5%. By contrast, the messaging system achieves
much higher energy-saving actions by presenting users with only
relevant and immediately actionable suggestions on how to cut down
power consumption in their immediate office/cubicle. The system
leverages behavioral science, customer data analytics, and the
latest software to engage employees of utilities and energy
consumers to collectively take action to save energy. The system
enables energy consumers to increase energy efficiency, reduce
costs, and realize environmental benefits. The system can: [0100]
Collect detailed occupancy/usage data with a combination of
sub-meters and low cost sensors [0101] Create models of occupancy
patterns (Daily Office Activities) [0102] Visualize usage data
[0103] Apply occupancy models with sensor data to automatically
control HVAC/heating/lighting/appliances to save energy [0104]
Predict demand and communicate with utility computers during peak
load [0105] Prompting of building occupants for energy-saving
actions.
[0106] The system can compare a consumer's energy usage with
similar energy consumption from his or her neighbors, and then
select based on the comparison, a message to be provided to the
consumer. The system can determine the relevant population that the
consumer belongs to for comparison purposes. The relevant
population can be based on geography, such as a city name, postal
code, or both. The system can select a normative message from a
plurality of candidate messages. The message selection can include
assigning to each of at least a subset of a plurality of candidate
messages a priority and selecting based at least in part on the
assigned priorities a number of selected messages, wherein the
number of messages selected corresponds to a limited number of
messages to be presented to the consumer. The system can receive
feedback indicative of an effectiveness of the message wherein the
message is selected based at least in part on the feedback.
Feedback data includes usage of at least the relevant population
and the consumer. Feedback data includes consumer action taken with
respect to the message. The system can determine usage of the
resource as one or more of the following: a time-value curve, a
mean usage, a median usage, an average usage, and an aggregate
usage. The message to be provided to the consumer is part of the
consumer's resource bill, the resource's website, or both.
[0107] The system can communicate a consumer's usage of an energy
resource. First, a relevant group is determined. In some
embodiments this may be omitted if a relevant member of the similar
group is pre-calculated or determined externally. In some
embodiments, determining the relevant group can include selecting
the relevant group based at least in part on a determination that
the consumer's usage of energy is greater than the relevant members
of the similar group's usage of energy resource. Selecting the
relevant members of the similar group can include comparing the
consumer's usage to that of each of a plurality of candidate
members of the similar groups and selecting as the relevant members
of the similar group the candidate members of the similar group to
which the consumer compares least favorably. In some embodiments,
determining the relevant members of the similar group can include
using third party data sources. For example, third party data
sources may include records associated with home ownership, which
are used to identify relevant members of the similar group based at
least in part on information indicating such members own a home
associated with their consumption of the resource. The consumer's
usage and relevant members of the similar group's usage of the
resource are compared. The usage of the resource may be time-value
curve or a statistical measure such as a mean, median, average, or
aggregate usage. In some embodiments, the usage is chosen at least
in part so that the consumer's usage of the resource is greater
than the relevant members of the similar group's usage of the
resource. The comparison is communicated to the consumer. In some
embodiments, the comparison is communicated to the consumer as
integrated with the consumer's resource bill, standalone with the
consumer's resource bill or on the resource's website under the
consumer's web account.
[0108] Targeted direct marketing techniques can be used to persuade
a consumer to moderate resource consumption using one or more of
these techniques: [0109] segmentation of the set of consumers into
different subsets based upon a plurality of demographic variables;
[0110] segmentation of the set of consumers into different subsets
based upon analysis and characterization of energy usage normalized
to relevant peer groups; [0111] prioritization of the messages
based upon their historical rate of uptake multiplied by the
expected energy savings value of the program; [0112] offers and
services for resource efficient products discounted by private
industry through rebates, coupons, and other discounts to support
government subsidies of efficient products; [0113] high quality
design (using high quality print design, high quality web graphics,
video, audio and other multimedia) for all data reports,
dynamically customized for each consumer; [0114] integration with
an Internet site or website for online and offline viewing of
reports; [0115] scalability of report format to hundreds of
millions of reports; [0116] enabling efficacy tracking of hundreds
of simultaneous marketing and messaging campaigns; and [0117]
straightforward integration with resource and/or utility
databases.
[0118] In one example, a relevant group for a consumer could be
"3-bedroom houses on the consumer's street". The system may have
data that over a twelve month average, the consumer used 66% more
electrical energy than the relevant group. Another example can
include data that one or more members of the relevant group
recently participated in a air conditioner efficiency rebate
program, or that the consumer's electricity usage time-value curve
coupled with a temperature time-value curve indicates that the
consumer's electricity usage is higher than average during hot
weather. In some embodiments, a similar analysis would determine
whether a consumer's electricity usage increases as a percentage of
daily use more than average during hot weather.
[0119] In some embodiments, the system can take a global list of
possible candidate messages and filters out and prioritizes
messages to be sent to the consumer. For example, the long global
list of possible candidate messages may include a message to
"install efficient central air conditioning using an existing
government rebate", a message to "install a timer for a car engine
block heater during winter". In the above example where the input
data shows that a consumer's electricity usage is higher than
average during hot weather, and that 39% of the relevant group
members have participated in an air conditioner rebate program, the
system may prioritize the "install efficient central air
conditioning using an existing government rebate" candidate message
higher than "install a timer for a car engine block heater during
winter" candidate message, especially if another data indicates the
consumer and relevant group members live in a state where there are
no winters below freezing. Feedback is used to determine the
effectiveness of the algorithms used in the messaging module to
determine appropriate selected messages. In some embodiments,
feedback includes usage of at least the relevant group members and
the consumer, to see if any or no change has occurred since the
last communication.
[0120] In some embodiments, feedback includes consumer action taken
with respect to the message, for example if a consumer has since
participated in an air conditioner rebate program. In some
embodiments, feedback includes an estimate of future usage of the
relevant group and the consumer based on previous consumer action
participation.
[0121] For utilities, the deployment of smart metering technology
results in a flow of data several magnitudes greater than any
previous traditional metering schemes. This increased data volume
will not only flow into the managing utility, but may also be
passed to and from third-party retailers for processing under new
and modified market transactions. The need to manage this data, and
subsequently transform it into actionable business intelligence,
creates challenges for utilities implementing smart metering. To
meet these challenges, in one embodiment, a load disaggregation
meter data management systems provides utilities with a
business-critical solution for storing, validating, aggregating and
processing large volumes of data, in preparation for billing,
settlements and other reporting and reconciliation obligations. In
some markets, there will also be requirements for timely delivery
of aggregated data to the market. In one embodiment, the system
runs on a cloud computer that securely connects to a utility data
center. In another embodiment, the system runs on a computer in the
utility data center. The system provides "intelligence" that can be
derived from smart meters and other smart grid devices so that
utilities can derive the substantial benefits that smart grid
deployments can deliver. As these deployments significantly
increase data quantity & availability, the computer providing
load disaggregation data analytics is essential.
[0122] The system accesses the utility's centralized data
repository for meter readings. Adapters are provided to collection
systems that enable raw data collected from smart meters to be
loaded into the load disaggregator, while also enabling controls to
be performed. The load disaggregator allows meter read management
components to validate, estimate, edit (VEE) and apply
utility-specific or regulation specific business logic to meter
readings. An engine is provided to calculate energy usage, demand
and other bill determinants. Adapters are provided to link in to
downstream systems that consume processed meter data, such as
billing, settlements, load forecasting, asset management and
customer Web portals.
[0123] Emerging trends, such as demand response and distributed
generation, introduce potential complexities in meter data
management and billing that may expand the capabilities required
from utility data centers. For instance, the need to support
residential demand-response programs may require the ability to
evaluate customer participation using: Demand-response event
information; Customer override of load control reported by in-home
devices; Customer baseline calculations using sophisticated
methodologies that compare a number of similar nonevent days
adjusted for weather; ability to perform "net settlement" functions
(whereby the consumer is compensated for energy delivered onto the
grid using a separate generation tariff). Distributed generation
programs will also require additional capabilities. Allowing homes,
farms and businesses to generate their own power from renewable
sources, (such as wind, water, solar and agricultural biomass) and
distributing any excess electricity back to the grid for credit
will require: The ability to meter and store at least two channels
of energy interval data (import and export values) for all
customers. Net metering (consumer is billed for net energy use
during the various tiers). Validation and estimation routines can
account for energy imports from customers (and can accommodate
negative net energy usage in an interval). Association of
generation pricing tariffs to customer accounts. Utilities whose
business drivers include billing, customer service and efficacy
analysis for their demand-response and distributed generation
programs can use the load disaggregation computer to provide these
benefits.
[0124] The load disaggregation computer can handle widespread
propagation and/or concentration of distributed generation on the
distribution network. For example, utility support programs
allowing homes, farms and businesses to generate their own power
from renewable sources--wind, water, solar power, agricultural
biomass--and send excess electricity back to the grid for credit,
and the eventual mass adoption of plug-in electric vehicles that
can act as distributed generation resources during peak periods.
These diverse distributed generation resources typically use
inverter-based technologies. Large concentrations, defined by some
industry studies 3,4 as more than 10 percent of serviced premises
on a feeder, or propagation of distributed generation on rural,
low-density feeders, can result in a variety of problems around
power Integration of distributed generation.
[0125] The system allows the smart meter network to act as the
communications network required to create and implement a smarter
distribution grid. New devices, such as transformer and feeder
meters, are becoming integral elements of smart grid deployments.
Utilities may also need to track in-home devices, such as
thermostats and load control switches--which may not be the
utility's own assets--and their life cycles, as part of device and
configuration management. Furthermore, many of these new devices
are expected to be capable of remote configuration and
reprogramming. The load disaggregator can work with grid monitoring
equipment, such as transformer meters and feeder meters, to enable
utilities to maintain accurate information about the distribution
network hierarchy.
[0126] In the context of bi-directional smart metering
infrastructure networks, the load disaggregator can act as the
routing and management component for implementing two-way
processes. For example, the system can provide "turn-on/turn-off"
processes at a utility using a combination of manual processes and
smart meters with an integrated remote connect disconnect (RCD)
switch. In this case, once the load disaggregator and a customer
information system determine that customer power is to be turned
off, the system can determine, depending on the meter type, whether
the turn-on/turn-off requires a field service order, or can be
executed directly through the smart metering infrastructure
systems. Other examples of process automation enabled by the load
disaggregator include: On-demand reads initiated by customer
service; Outage pings; Smart meter configuration and firmware
upgrade management; Demand-response event orchestration and
management. For exception monitoring, reporting and management, the
system can subscribe to events, status messages, alarms and alerts
from automated metering infrastructure to provide real-time
monitoring of the network and field devices. The information
provided can generate insight into operational issues, the health
of devices and analysis of operational trends. Examples include:
Use of reported meter health events to dispatch meter technicians
to the field and review trends that may indicate quality issues
with a particular batch or type of meter; Detection of tamper and
theft from "unexpected" tilt indicators; Analysis of momentary
outage indicators reported by meters on a distribution feeder or
secondary to identify the need for vegetation trimming; Integration
with intrusion detection systems to notify a potential security
breach in the smart metering infrastructure network (such as
unauthorized access at the meter's optical probe); and Calculation
and reporting of reliability indices from smart meter outage and
restoration information.
[0127] The system provides advanced asset management which is the
ability to manage the operational state and performance of assets
on the distribution network. By combining information about the
distribution network topology with data from new smart-grid
devices--such as transformer meters, low-voltage and medium voltage
sensors (feeder meters) and metered data from smart meters and grid
sensors--utilities can develop a wide array of monitoring,
analytical and visualization applications. In combination with load
disaggregation, these applications provide the distribution control
center with a much higher degree of situational awareness.
Distribution system planning groups can also use the same
information to achieve a number of benefits. These include
understanding the operational characteristics (such as loading,
losses, phase imbalance and utilization) of the distribution
network assets, optimizing the utilization of existing assets and
the ability to defer capital expenditure for new assets. The load
disaggregation thus can provide the ability to track grid assets,
network hierarchy and data reported by grid devices.
[0128] The system allows utilities to offer additional products and
services such as providing a low-cost, comprehensive, real-time
monitoring of customer's vital daily life activities. Information
can be viewed using an Internet-based website, a personal computer,
or simply by viewing a display on the monitor. Data measured
several times each day provide a relatively comprehensive data set
compared to that measured during medical appointments separated by
several weeks or even months.
[0129] While this invention has been particularly shown and
described with references to preferred embodiments thereof, it will
be understood by those skilled in the art that various changes in
form and details may be made therein without departing from the
spirit and scope of the invention as defined by the appended
claims.
* * * * *